4.7 Article

Insights into the intraparticle morphology of dendritic mesoporous silica nanoparticles from electron tomographic reconstructions

期刊

JOURNAL OF COLLOID AND INTERFACE SCIENCE
卷 592, 期 -, 页码 296-309

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcis.2021.02.069

关键词

Dendritic mesoporous silica nanoparticles; Particle size; Pore size distribution; Mesoporosity; Pore shape; Morphology; Hierarchical structure; Electron tomography

资金

  1. Karlsruhe Nano Micro Facility (KNMF) at the Karlsruhe Institute of Technology (Karlsruhe, Germany) under the KNMF [2020-024-029294]
  2. University of Vienna, Austria

向作者/读者索取更多资源

The study focuses on fine-tuning the morphology of dendritic mesoporous silica nanoparticles (DMSNs) and successfully demonstrates the independent tuning of particle and pore sizes. The unique morphological features influencing guest molecule interactions were uncovered through detailed analysis.
Hypothesis: Although many synthetic pathways allow to fine-tune the morphology of dendritic mesoporous silica nanoparticles (DMSNs), the control of their particle size and mesopore diameter remains a challenge. Our study focuses on either increasing the mean particle size or adjusting the pore size distribution, changing only one parameter (particle or pore size) at a time. The dependence of key morphological features (porosity; pore shape and pore dimensions) on radial distance from the particle center has been investigated in detail.& nbsp; Experiments: Three-dimensional reconstructions of the particles obtained by scanning transmission electron microscopy (STEM) tomography were adapted as geometrical models for the quantification of intraparticle morphologies by radial porosity and chord length distribution analyses. Structural properties of the different synthesized DMSNs have been complementary characterized using TEM, SEM, nitrogen physisorption, and dynamic light scattering.& nbsp; Findings: The successful independent tuning of particle and pore sizes of the DMSNs could be confirmed by conventional analysis methods. Unique morphological features, which influence the uptake and release of guest molecules in biomedical applications, were uncovered from analyzing the STEM tomography-based reconstructions. It includes the quantification of structural hierarchy, identification of intrawall openings and pores, as well as the distinction of pore shapes (conical vs. cylindrical).& nbsp; (c) 2021 Elsevier Inc. All rights reserved. Hypothesis: Although many synthetic pathways allow to fine-tune the morphology of dendritic mesoporous silica nanoparticles (DMSNs), the control of their particle size and mesopore diameter remains a challenge. Our study focuses on either increasing the mean particle size or adjusting the pore size distribution, changing only one parameter (particle or pore size) at a time. The dependence of key morphological features (porosity; pore shape and pore dimensions) on radial distance from the particle center has been investigated in detail. Experiments: Three-dimensional reconstructions of the particles obtained by scanning transmission electron microscopy (STEM) tomography were adapted as geometrical models for the quantification of intraparticle morphologies by radial porosity and chord length distribution analyses. Structural properties of the different synthesized DMSNs have been complementary characterized using TEM, SEM, nitrogen physisorption, and dynamic light scattering. Findings: The successful independent tuning of particle and pore sizes of the DMSNs could be confirmed by conventional analysis methods. Unique morphological features, which influence the uptake and release of guest molecules in biomedical applications, were uncovered from analyzing the STEM tomography-based reconstructions. It includes the quantification of structural hierarchy, identification of intrawall openings and pores, as well as the distinction of pore shapes (conical vs. cylindrical).

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据